Optimizing Bucket Elevator Performance through a Blend of Discrete Element Method, Response Surface Methodology, and Firefly Algorithm Approaches

This research introduces a novel approach to enhancing bucket elevator design and operation through the integration of discrete element method(DEM)simulation,design of experiments(DOE),and metaheuristic optimization algorithms.Specifically,the study employs the firefly algorithm(FA),a metaheuristic optimization technique,to optimize bucket elevator parameters for maximizing transport mass and mass flow rate discharge of granular materials under specified working conditions.The experimental methodology involves several key steps:screening experiments to identify significant factors affecting bucket elevator operation,central composite design(CCD)experiments to further explore these factors,and response surface methodology(RSM)to create predictive models for transport mass and mass flow rate discharge.The FA algorithm is then applied to optimize these models,and the results are validated through simulation and empirical experiments.The study validates the optimized parameters through simulation and empirical experiments,comparing results with DEM simulation.The outcomes demonstrate the effectiveness of the FA algorithm in identifying optimal bucket parameters,showcasing less than 10%and 15%deviation for transport mass and mass flow rate discharge,respectively,between predicted and actual values.Overall,this research provides insights into the critical factors influencing bucket elevator operation and offers a systematic methodology for optimizing bucket parameters,contributing to more efficient material handling in various industrial applications.

Shape and Size Optimization of Truss Structures under Frequency Constraints Based on Hybrid Sine Cosine Firefly Algorithm

Shape and size optimization with frequency constraints is a highly nonlinear problem withmixed design variables,non-convex search space,and multiple local optima.Therefore,a hybrid sine cosine firefly algorithm(HSCFA)is proposed to acquire more accurate solutions with less finite element analysis.The full attraction model of firefly algorithm(FA)is analyzed,and the factors that affect its computational efficiency and accuracy are revealed.A modified FA with simplified attraction model and adaptive parameter of sine cosine algorithm(SCA)is proposed to reduce the computational complexity and enhance the convergence rate.Then,the population is classified,and different populations are updated by modified FA and SCA respectively.Besides,the random search strategy based on Lévy flight is adopted to update the stagnant or infeasible solutions to enhance the population diversity.Elitist selection technique is applied to save the promising solutions and further improve the convergence rate.Moreover,the adaptive penalty function is employed to deal with the constraints.Finally,the performance of HSCFA is demonstrated through the numerical examples with nonstructural masses and frequency constraints.The results show that HSCFA is an efficient and competitive tool for shape and size optimization problems with frequency constraints.

Levy Flight Firefly Based Efficient Resource Allocation for Fog Environment

Fog computing is an emergent and powerful computing paradigm to serve latency-sensitive applications by executing internet of things(IoT)appli-cations in the proximity of the network.Fog computing offers computational and storage services between cloud and terminal devices.However,an efficient resource allocation to execute the IoT applications in a fog environment is still challenging due to limited resource availability and low delay requirement of services.A large number of heterogeneous shareable resources makes fog computing a complex environment.In the sight of these issues,this paper has proposed an efficient levy flight firefly-based resource allocation technique.The levy flight algorithm is a metaheuristic algorithm.It offers high efficiency and success rate because of its longer step length and fast convergence rate.Thus,it treats global optimization problems more efficiently and naturally.A system framework for fog computing is presented,followed by the proposed resource allocation scheme in the fog computing environment.Experimental evaluation and comparison with the firefly algorithm(FA),particle swarm optimization(PSO),genetic algorithm(GA)and hybrid algorithm using GA and PSO(GAPSO)have been conducted to validate the effectiveness and efficiency of the proposed algorithm.Simulation results show that the proposed algorithm performs efficient resource allocation and improves the quality of service(QoS).The proposed algorithm reduces average waiting time,average execution time,average turnaround time,processing cost and energy consumption and increases resource utilization and task success rate compared to FA,GAPSO,PSO and GA.

Fuzzy Firefly Based Intelligent Algorithm for Load Balancing inMobile Cloud Computing

This paper presents a novel fuzzy firefly-based intelligent algorithm for load balancing in mobile cloud computing while reducing makespan.The proposed technique implicitly acts intelligently by using inherent traits of fuzzy and firefly.It automatically adjusts its behavior or converges depending on the information gathered during the search process and objective function.It works for 3-tier architecture,including cloudlet and public cloud.As cloudlets have limited resources,fuzzy logic is used for cloudlet selection using capacity and waiting time as input.Fuzzy provides human-like decisions without using any mathematical model.Firefly is a powerful meta-heuristic optimization technique to balance diversification and solution speed.It balances the load on cloud and cloudlet while minimizing makespan and execution time.However,it may trap in local optimum;levy flight can handle it.Hybridization of fuzzy fireflywith levy flight is a novel technique that provides reduced makespan,execution time,and Degree of imbalance while balancing the load.Simulation has been carried out on the Cloud Analyst platform with National Aeronautics and Space Administration(NASA)and Clarknet datasets.Results show that the proposed algorithm outperforms Ant Colony Optimization Queue Decision Maker(ACOQDM),Distributed Scheduling Optimization Algorithm(DSOA),andUtility-based Firefly Algorithm(UFA)when compared in terms of makespan,Degree of imbalance,and Figure of Merit.

Firefly-CDDL:A Firefly-Based Algorithm for Cyberbullying Detection Based on Deep Learning

There are several ethical issues that have arisen in recent years due to the ubiquity of the Internet and the popularity of social media and community platforms.Among them is cyberbullying,which is defined as any violent intentional action that is repeatedly conducted by individuals or groups using online channels against victims who are not able to react effectively.An alarmingly high percentage of people,especially teenagers,have reported being cyberbullied in recent years.A variety of approaches have been developed to detect cyberbullying,but they require time-consuming feature extraction and selection processes.Moreover,no approach to date has examined the meanings of words and the semantics involved in cyberbullying.In past work,we proposed an algorithm called Cyberbullying Detection Based on Deep Learning(CDDL)to bridge this gap.It eliminates the need for feature engineering and generates better predictions than traditional approaches for detecting cyberbullying.This was accomplished by incorporating deep learning—specifically,a convolutional neural network(CNN)—into the detection process.Although this algorithm shows remarkable improvement in performance over traditional detection mechanisms,one problem with it persists:CDDL requires that many parameters(filters,kernels,pool size,and number of neurons)be set prior to classification.These parameters play a major role in the quality of predictions,but a method for finding a suitable combination of their values remains elusive.To address this issue,we propose an algorithm called firefly-CDDL that incorporates a firefly optimisation algorithm into CDDL to automate the hitherto-manual trial-and-error hyperparameter setting.The proposed method does not require features for its predictions and its detection of cyberbullying is fully automated.The firefly-CDDL outperformed prevalent methods for detecting cyberbullying in experiments and recorded an accuracy of 98%within acceptable polynomial time.

Robust Image Watermarking Using LWT and Stochastic Gradient Firefly Algorithm

Watermarking of digital images is required in diversified applicationsranging from medical imaging to commercial images used over the web.Usually, the copyright information is embossed over the image in the form ofa logo at the corner or diagonal text in the background. However, this formof visible watermarking is not suitable for a large class of applications. In allsuch cases, a hidden watermark is embedded inside the original image as proofof ownership. A large number of techniques and algorithms are proposedby researchers for invisible watermarking. In this paper, we focus on issuesthat are critical for security aspects in the most common domains like digitalphotography copyrighting, online image stores, etc. The requirements of thisclass of application include robustness (resistance to attack), blindness (directextraction without original image), high embedding capacity, high Peak Signalto Noise Ratio (PSNR), and high Structural Similarity Matrix (SSIM). Mostof these requirements are conflicting, which means that an attempt to maximizeone requirement harms the other. In this paper, a blind type of imagewatermarking scheme is proposed using Lifting Wavelet Transform (LWT)as the baseline. Using this technique, custom binary watermarks in the formof a binary string can be embedded. Hu’s Invariant moments’ coefficientsare used as a key to extract the watermark. A Stochastic variant of theFirefly algorithm (FA) is used for the optimization of the technique. Undera prespecified size of embedding data, high PSNR and SSIM are obtainedusing the Stochastic Gradient variant of the Firefly technique. The simulationis done using Matrix Laboratory (MATLAB) tool and it is shown that theproposed technique outperforms the benchmark techniques of watermarkingconsidering PSNR and SSIM as quality metrics.

A Modified Firefly Optimization Algorithm-Based Fuzzy Packet Scheduler for MANET

In Mobile ad hoc Networks(MANETs),the packet scheduling process is considered the major challenge because of error-prone connectivity among mobile nodes that introduces intolerable delay and insufficient throughput with high packet loss.In this paper,a Modified Firefly Optimization Algorithm improved Fuzzy Scheduler-based Packet Scheduling(MFPA-FSPS)Mechanism is proposed for sustaining Quality of Service(QoS)in the network.This MFPA-FSPS mechanism included a Fuzzy-based priority scheduler by inheriting the merits of the Sugeno Fuzzy inference system that potentially and adaptively estimated packets’priority for guaranteeing optimal network performance.It further used the modified Firefly Optimization Algorithm to optimize the rules uti-lized by the fuzzy inference engine to achieve the potential packet scheduling pro-cess.This adoption of a fuzzy inference engine used dynamic optimization that guaranteed excellent scheduling of the necessitated packets at an appropriate time with minimized waiting time.The statistical validation of the proposed MFPA-FSPS conducted using a one-way Analysis of Variance(ANOVA)test confirmed its predominance over the benchmarked schemes used for investigation.

Automatic Sentimental Analysis by Firefly with Levy and Multilayer Perceptron

The field of sentiment analysis(SA)has grown in tandem with the aid of social networking platforms to exchange opinions and ideas.Many people share their views and ideas around the world through social media like Facebook and Twitter.The goal of opinion mining,commonly referred to as sentiment analysis,is to categorise and forecast a target’s opinion.Depending on if they provide a positive or negative perspective on a given topic,text documents or sentences can be classified.When compared to sentiment analysis,text categorization may appear to be a simple process,but number of challenges have prompted numerous studies in this area.A feature selection-based classification algorithm in conjunction with the firefly with levy and multilayer perceptron(MLP)techniques has been proposed as a way to automate sentiment analysis(SA).In this study,online product reviews can be enhanced by integrating classification and feature election.The firefly(FF)algorithm was used to extract features from online product reviews,and a multi-layer perceptron was used to classify sentiment(MLP).The experiment employs two datasets,and the results are assessed using a variety of criteria.On account of these tests,it is possible to conclude that the FFL-MLP algorithm has the better classification performance for Canon(98%accuracy)and iPod(99%accuracy).

Optimization of Cognitive Radio System Using Enhanced Firefly Algorithm

The optimization of cognitive radio(CR)system using an enhanced firefly algorithm(EFA)is presented in this work.The Firefly algorithm(FA)is a nature-inspired algorithm based on the unique light-flashing behavior of fireflies.It has already proved its competence in various optimization prob-lems,but it suffers from slow convergence issues.To improve the convergence performance of FA,a new variant named EFA is proposed.The effectiveness of EFA as a good optimizer is demonstrated by optimizing benchmark functions,and simulation results show its superior performance compared to biogeography-based optimization(BBO),bat algorithm,artificial bee colony,and FA.As an application of this algorithm to real-world problems,EFA is also applied to optimize the CR system.CR is a revolutionary technique that uses a dynamic spectrum allocation strategy to solve the spectrum scarcity problem.However,it requires optimization to meet specific performance objectives.The results obtained by EFA in CR system optimization are compared with results in the literature of BBO,simulated annealing,and genetic algorithm.Statistical results further prove that the proposed algorithm is highly efficient and provides superior results.

High-quality Development Path of Night Tourism in Guangzhou:A Case Study of Firefly Night Tour

Night tourism often involves a large number of lighting facilities,which consume a large amount of energy.Therefore,one of the unique low energy consumption natural ecological tourism activities—firefly night tour has attracted attention and become an important breakthrough point for night tourism in tourist destinations.In this paper,Guangzhou firefly night tour project is taken as the research object.Based on the comprehensive economic,environmental,and socio-cultural benefits brought by the development of firefly night tour,the resources distribution,current development status,and existing problems of firefly night tour in Guangzhou are analyzed,and its high-quality development paths are proposed from three levels:government,industry,and tourist.The aim is to explore a new model for the economic development of Guangzhou night tour,boosting the transformation and upgrading of the night tourism economy,while also providing reference ideas and value for the development of night tourism economy in other tourist destinations.

分布式软件定义网络中多域流量工程的路由优化方法

针对分布式软件定义网络(software-defined networking,SDN)中流量管理调度不均衡的流量工程问题,提出一种基于负载均衡的多控制域流量路由优化的解决方案。首先分析控制消息流量的组成、域内通信及域间通信规则;然后基于4种控制消息定义控制链路流量的构成,明确链路承载流量分为控制消息流量和业务流量,建立平衡控制器负载和最小化最大链路利用率的优化模型;最后基于域内通信和域间通信提出两层路由算法。为提高模型求解精度,进一步提出改进离散萤火虫算法求解最优路由。结合ABILENE网络和GEANT网络,分析控制消息流量、控制器负载和链路负载等评价指标。实验结果表明,优化模型能有效实现控制器和链路负载均衡,控制消息流量是流量工程重要组成部分。相比集中控制模式,扁平分布式控制模式的平均控制器负载降低47.3%,最大链路利用率相差不超过15%。

层级引导的增强型多目标萤火虫算法

针对多目标萤火虫算法在求解过程中易产生振荡和聚集现象,导致开发能力较弱、求解精度不佳的问题,提出一种层级引导的增强型多目标萤火虫算法(hierarchical guided enhanced multi-objective firefly algorithm,HGEMOFA)。构建层级引导模型,利用非支配排序获得不同层级个体,用优势层个体引导劣势层个体进化,明确引导方向,解决了进化过程中出现的振荡,减少了聚集现象的出现,增强了算法收敛性;引入莱维飞行扰动最优层个体,增强算法的全局搜索能力;每代进化完成后,对当前种群采用变异机制,增强算法的局部开发能力;把变异后的种群和前一代种群合并进行环境选择,筛选出和前一代种群规模相同的子代,避免优势解丢失。实验结果表明:HGEMOFA能有效增强解的收敛性和多样性。

Adaptive Kernel Firefly Algorithm Based Feature Selection and Q-Learner Machine Learning Models in Cloud

CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose significance.MLTs(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferring information.A dynamic strategy,DevMLOps(Development Machine Learning Operations)used in automatic selections and tunings of MLTs result in significant performance differences.But,the scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution times.RFEs(Recursive Feature Eliminations)are computationally very expensive in its operations as it traverses through each feature without considering correlations between them.This problem can be overcome by the use of Wrappers as they select better features by accounting for test and train datasets.The aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between containers.The proposed AKFA(Adaptive Kernel Firefly Algorithm)is for selecting features for CNM(Cloud Network Monitoring)operations.AKFA methodology is demonstrated using CNSD(Cloud Network Security Dataset)with satisfactory results in the performance metrics like precision,recall,F-measure and accuracy used.

基于等效模型的砖石古塔有限元模型修正方法

针对砖石古塔实体有限元模型节点众多导致修正效率低下问题,提出了一种将实体模型和杆系模型相结合的有限元模型快速修正方法。基于古塔结构的测试输出信号,使用随机子空间法识别了塔的自振频率。使用整体连续式方法建立了ABAQUS有限元模型,基于实测结果,使用萤火虫算法修正了塔的弹性模量、密度。修正后的有限元模型的计算结果与实测结果对比,能够较好吻合,说明修正后的模型可靠,可以作为后续抗震性能分析的基础。

基于混沌求偶萤火虫算法的移动机器人路径规划

针对传统萤火虫算法应用于移动机器人路径规划中存在陷入局部最优和搜索精度低的问题,提出一种基于混沌求偶萤火虫算法的移动机器人路径规划方法。设计一种混沌求偶荧火虫算法,该算法采用混沌映射策略初始化种群,优化种群分布不均和搜索范围不足问题;利用求偶学习策略指导雄性萤火虫向雌性萤火虫学习,提高算法的收敛速度和求解精度。建立移动机器人路径规划的环境仿真模型,应用混沌求偶萤火虫算法进行移动机器人路径规划仿真。仿真结果表明:混沌求偶萤火虫算法比传统萤火虫算法和粒子群算法在路径长度上分别减少了3.075%和2.428%,拥有更高的搜索精度和跳出局部最优的能力。

基于FA-BP神经网络的生姜干燥含水率预测

为探索生姜的干燥特性,并实现生姜干燥的含水率预测,研究了不同干燥温度(50、55、60℃)、干燥风速(1.0、2.0、3.0m/s)、切片长度(30、35、40mm)对生姜干燥时间和干燥速率的影响。结合BP神经网络自适应能力、泛化能力、学习能力强和萤火虫算法(FA)参数少、寻优能力强、收敛速度快等特点,将干燥温度、干燥风速、切片长度和干燥时间作为输入层,隐藏层个数为10,输出层为生姜的含水率,搭建一个拓扑结构为“4-10-1”的FA-BP神经网络模型。研究结果表明:干燥温度、干燥风速、切片长度都是影响生姜含水率的关键因素,增加干燥风速、提高干燥温度和减少切片长度能有效缩短生姜的干燥时间,提高干燥效率。选用萤火虫算法优化BP神经网络的权值和阈值,减少了神经网络的训练时间,提高了精准度,其含水率预测值与试验值之间的决定系数R2=0.999 02,均方根误差RMSE为0.002 99,含水率预测结果准确且迅速,能够为生姜干燥过程中的含水率在线预测提供科学依据。

基于ikPCA-FABAS-KELM的短期风电功率预测

为了增强在短期风电功率预测领域中传统数据驱动机器学习模型的精度,提出基于ikPCA-FABAS-KELM的短期风电功率预测模型.首先,对主成分分析进行改进,提出可逆核主成分分析(ikPCA),在保证数据特征的同时,降低输入数据的复杂度,以提升模型运行速度;其次,引入萤火虫个体吸引策略对天牛须算法(BAS)进行改进,提出FABAS算法;最后,利用FABAS算法对核极限学习机(KELM)的正则化参数C和核参数γ进行寻优,降低人为因素对模型盲目训练的影响,提高模型预测精度.仿真结果显示,提出的预测模型有效提高了传统模型的预测精度.

基于改进萤火虫算法的永磁同步电机多模态优化设计

永磁同步电机的结构参数选择往往是多模态、非线性的优化问题,同时其结构优化设计又存在计算分析时间过长、计算效率过低、收敛慢等问题。文中针对此类问题提出了一种基于改进萤火虫算法的新型多模态优化算法。改进萤火虫算法主要引用了等值线概念并通过克里金(Kriging)代理模型自适应确定子计算区域,从而获得多个全局最优解和局部解,并且引入单纯形法和改进停止条件以提高算法效率。此外,研究了分层余一法用于检验代理模型精度。最后,将其应用到永磁同步电机的结构优化中,使齿槽转矩成功降低了38.33%。

基于强化学习的离散层级萤火虫算法检测蛋白质复合物

蛋白质复合物的检测有助于从分子水平上理解生命的活动过程。针对群智能算法检测蛋白质复合物时假阳/阴性率高、准确率低、种群多样性下降等问题,提出了基于强化学习的离散层级萤火虫算法检测蛋白质复合物(reinforcement learning-based discrete level firefly algorithm for detecting protein complexes,RLDLFA-DPC)。引入强化学习思想提出一种自适应层级划分策略,动态调整层级结构,能有效解决迭代后期种群多样性下降的问题。在层级学习策略中个体向两个优秀层级学习,避免算法陷入局部最优。为了提高蛋白质复合物检测的精度,结合个体环境信息提出自适应搜索半径的局部搜索策略。最后,在酵母蛋白质的4个数据集上,与8种经典的蛋白质复合物检测方法进行对比,验证了该方法的有效性。

基于互信息与萤火虫算法的网络入侵特征选择

为减少网络入侵检测数据中的冗余特征,提出一种结合互信息和萤火虫算法的特征选择方法。针对互信息不能精确计算特征间冗余度,提出类内特征冗余互信息特征选择方法。针对萤火虫算法步长因子固定易使算法陷入局部最优等问题,提出自适应步长萤火虫算法特征选择。以上方法分别选取特征子集后利用投票策略选取最优子集,对该子集基于C4.5和贝叶斯网络分类器分类。实验结果表明,使用10个特征检测能有效提高入侵检测率、误报率和F-measure,同时还缩短训练和检测时间。此外,与现有的几种方法相比,该方法在准确率、检测率和F-measure都获得不错效果。